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Systematized Healthcare systems with Artificial Intelligence

Artificial Intelligence is highly adopted by the health care industry for care and case study. It has helped physicians to collaborate more effectively with patients, early detection of diseases, better treatment techniques, save the time required for storing and analyzing patient’s data. Apart from reducing costs, identity protection, and improving process, AI can efficiently help with a few things like determining the most effective pharmaceutical compositions. AI has the ability to process data from multiple sources and provide predictive analytics that will drive the best path of care.

Virtual medicine

Virtual medicine is connecting rural patients to leading urban health centers, significantly shortening specialist wait times, and reducing readmission rates. It has the potential to help providers monitor their patients remotely through AI-enabled tools.

Assisting Providers

The goal of AI in healthcare is to make a highly precise diagnosis within a short period of time. Sensitive and specific algorithms can now be trained on datasets to support medical professionals who screen for pathologies with specialist-level accuracy. These AI, Machine Learning, and Deep Learning (DL) algorithms will improve with time and will play a critical role in healthcare in the future.

Blockchain

Blockchain systems will be used to transfer data across health systems in ways that the Electronic Medical Records (EMR) cannot. There are currently many limitations to EMR when it comes to their compatibility between different health systems. Health systems should be using the same EMR hosting system to connect and share information with one another. But, blockchain systems could transfer the information without those limitations. Using blockchain for transferring patient information across healthcare systems could help keep patient records more secure.

AI can process information from multiple resources to provide predictive analytics that will benefit patients and save time for providers. Algorithms can be programmed to input data from various sources like health records, wearables, and genetic tests to create deeply personalized and timely outputs, such as disease risk profiles. AI tools can combine knowledge of DNA-variation with symptoms documented in the EHR, such as chronic bloating or vomiting, and alert the physician.